Abstract
Currently the recognition of plants from leaves has been a field of research very studied, the current algorithms can perfectly classify the leaves of different families. However, for the current algorithms it is difficult to classify leaves belonging to the same family but different species, because they are very similar to each other that even experts have difficulties to make this classification. The next step in the leaf’s classification problem, is to classify the leaves that belong to the same family. Being this a problem that has not been targeted yet by the current literature. In this paper, we propose a method to extract the best features that describe the leaf, using a genetic algorithm. And then, testing these features with different classifiers and comparing its accuracy with two Convolutional Neural Networks models. The results demonstrated that the accuracy can be improved depending on the selected model and features.
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Ayala Niño, D., Ruíz Castilla, J.S., Arévalo Zenteno, M.D., D. Jalili, L. (2019). Complex Leaves Classification with Features Extractor. In: Huang, DS., Jo, KH., Huang, ZK. (eds) Intelligent Computing Theories and Application. ICIC 2019. Lecture Notes in Computer Science(), vol 11644. Springer, Cham. https://doi.org/10.1007/978-3-030-26969-2_72
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